# akg

**Repository Path**: qingfengli/akg

## Basic Information

- **Project Name**: akg
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-25
- **Last Updated**: 2021-03-25

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

- [What Is AKG?](#what-is-akg)
- [Hardware Backends Support](#hardware-backends-support)
- [Build](#build)
    - [Build With MindSpore](#build-with-mindspore)
    - [Build Standalone](#build-standalone)
- [Run](#run)
- [Contributing](#contributing)
- [Release Notes](#release-notes)
- [License](#license)

[查看中文](./README_CN.md)

## What Is AKG
AKG(Auto Kernel Generator) is an optimizer for operators in Deep Learning Networks. It provides the ability to automatically fuse ops with specific patterns. AKG works with MindSpore-GraphKernel to improve the performance of networks running on different hardware backends.

AKG composes with four basic optimization module, normalization, auto schedule, instruction emit and backend optimization.
- **normalization.** In order to solve the limitation in expression ability of polyhedral(which can only process static linear programs), the computation IR needs to be normalized first. The mainly optimization of normalization module includes auto-inline, loop partition, common subexpression elimination and so on.
- **auto schedule.** Base on polyhedral technology, the auto schedule module mainly have auto-vectorization, auto-tiling, dependency analysis and memory promotion.
- **instruction emit.** The instruction emitting module has the optimization about loop normalization, auto pragma and emit instruction.
- **backend optimization.** The backend optimization module consists of double buffer optimization, storage rewrite optimization and inject sync optimization.

  <img src="docs/akg-design.png" style="zoom:80%" div align=center/>

## Hardware Backends Support
At present, `Ascend910` and `GPU V100/A100` are supported. More Backends are on the list.

## Build

### Build With MindSpore
See [MindSpore README.md](https://gitee.com/mindspore/mindspore/blob/master/README.md) for details.

### Build Standalone
We suggest you build and run akg together with MindSpore. And we also provide a way to run case in standalone mode for convenience sake.
Refer to [MindSpore Installation](https://www.mindspore.cn/install/en) for more information about compilation dependencies.
  ```
  bash build.sh -t $target // target can set 'gpu' or 'ascend'
  ```

## Run Standalone
1. Set Environment

- Ascend910
  ```
  cd tests
  source ./test_env.sh amd64
  export RUNTIME_MODE='air_cloud'
  export PATH=${PATH}:${YOUR_CCEC_COMPILER_PATH}
  ```
- GPU V100/A100
  ```
  cd tests
  source ./test_env.sh gpu
  ```

2. Run test

- Ascend910
  ```
  cd tests/operators/vector
  pytest -s test_abs_001.py -m "level0" # run level0 testcases
  ```
- GPU V100/A100
  ```
  cd tests/operators/gpu
  python3 test_all.py -s "op_name" #replace op_name with the operator name which you want to test
  ```

## Contributing

Welcome contributions. See [MindSpore Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for
more details.

## Release Notes

The release notes, see our [RELEASE](RELEASE.md).

## License

[Apache License 2.0](LICENSE)